Lead Scoring: A Simple Model That Beats a Complicated Formula
A sales team with two hundred leads does not need a clever formula. They need to know who deserves the first call this morning.
Just two axes
Fit: how closely this lead resembles your best customers. Industry, size, location, the role of the person contacting you.
Interest: what they have already done. Filled a form, replied to a message, asked for a quote, attended a demo.
High fit and high interest get a call today. High fit with low interest goes into education. Low fit is not worth chasing, however interested they seem.
Derive it from your own data
Look at your last twenty wins and twenty losses. What traits separate them? Those are your fit criteria, not a generic list from any article.
Keep it simple
Five criteria is enough. A model with twenty weighted decimal criteria is not trusted by sales and will not be used.
Review quarterly
Compare the initial score with the final outcome. If low-scoring leads often win, the criteria are wrong and need fixing.
About automatic scoring
AI-based scoring can help once you have enough history. Before that, simple rules you wrote yourself are usually more accurate and far easier to explain to the team. See clean data before AI.
Connect it to action
A score that does not change the order of sales work is just a number. Make sure the daily work list is sorted by it. See follow-up automation.